With more companies developing quantitative teams, more professionals entering data science careers, and more blending between data scientists and others in predictive analytics (trends that we covered more extensively here), there are several further developments expected over the coming years:

Increasing Specialization of Roles

As additional companies cultivate data science and analytics teams, and many established teams grow larger, data science roles are starting to become more specialized. Larger teams are able to focus on hiring specialists who can collaborate, and therefore concentrate on fewer tasks, rather than all tasks at once. A firm hiring its first data scientist may, however, still seek the well-rounded (and expensive) “unicorn” who is able to complete every task including building infrastructure, data acquisition, modeling, communicating with the C-Suite, and more.

Many companies used to require that all of their data science hires had PhD’s, with a deep background both in math/statistics and computer science. Now, it is becoming more common to see many professionals on a large team with differing backgrounds – some in computer science, some in math and statistics, and others in engineering – all working together on the same data science project. With many quantitative teams continuing to grow and the data science “unicorns” being so difficult to hire, we expect the trend of specialization to continue.

Blending of Predictive Analytics & Data Science

This specialization is also somewhat influenced by predictive analytics professionals, many of whom have begun picking up the computer science skills necessary to transition into data science careers, which have a heavier focus on coding and unstructured data. The bigger teams are creating an environment which can support data scientists that may have less experience with typical data science tools, since they can work together with other team members, such as data engineers, to fill in the gaps.

We anticipate that, as the two disciplines continue to blend, the suppression of data science salaries will continue (you can download our study for more info on that) as they converge with predictive analytics salaries. Given the continuing popularity of the “data scientist” title, it’s possible that data science teams of the future will be home to modelers, data scientists, statisticians, and data engineers, and all of them will be paid on the same corporate scale. At some point, there may be no clear distinction between data scientists and other predictive analytics professionals in pay or in title, but it will likely be a number of years until we reach that point.

What You Can Do: Continuous Learning is Paramount

Data scientists and their teams must remain aware of the rapid and constant evolution of data science technology and tools. Continuous education for these professionals is absolutely critical to success, since new developments are being introduced much faster than in years past. We have always advocated that quantitative professionals stay close to the data and keep their skills sharp, and, now more than ever, it is crucial, especially for data science management. Data science managers who transition away from hands-on work altogether may quickly find their knowledge out-of-date and their skills irrelevant.

As always, we will continue to keep a close eye on developing data science career trends and the analytics hiring market. For more of our insights between the annual Burtch Works Studies, keep an eye on the blog!

Interested in our salary research on data scientists and predictive analytics professionals? Download our studies using the button below.

Learn more about the latest salaries and hiring market insights for the 2018 data science hiring market in our 10-minute Burtch Works Study recap video below, or watch the full-length version on YouTube!

6 Responses to “Predictions for the Future of Data Science Careers”

They say data science will be replaced by automation, I am training students for data science from 2 year and I don’t believe the same as I think automation can replace programming but the the core domain knowledge and thoughts of a data scientist that are to be put into. What is you say on this?